For a commercial retailer, the set of products carried in each store at a point in time can be referred to as the retailer's assortment. Assortment planning relates to specifying an assortment that maximizes a goal or goals such as sales revenue or gross margin, limited to certain restrains such as limited budget for purchasing products, limited shelf space for displaying products, and avoidance of single supplier situations.
The assortment of a retailer has a significant impact on sales and gross margin, and therefore assortment planning is seen as of high importance to many retailers. Some attempts have been made to provide commercial tools relating to assortment planning. For examples, see the Automated Micro Assortment Planner from Galleria Retail Technology Solution Ltd.; Torex Compass-SCM from Torex Retail Holding Ltd.; 7thOnline; SAP Retail Merchandise and Assortment Planning from SAP; and Oracle Retail Category Management from Oracle. However, none of these commercial techniques deals effectively with the effect of transfer of demand from one product to another product in the situation where the first product is not available to the customer.
Academic literature has proposed certain approaches for enhancing assortment planning including some research relating to an effective demand for a product including both the original demand for the product and substitution demand from other products. For example, see, “Assortment Planning: Review of Literature and Industry Practice,” A. G. Kok, M. L. Fisher, R Vaidyanathan (2006); and “Demand Estimation and Assortment Optimization Under Substitution: Methodology and Application,” A. G. Kok, M. L. Fisher, Operations Research, Vol. 55, No. 6, November-December 2007, pp. 1001-1021. However, the proposals described in the academic literature for research purposes make certain assumptions which limit their effective use in a commercial setting for assortment planning. For example, many of the approaches do not use commercial transaction data. Although the approach described in Kok and Fisher 2007 makes use of transaction data, the approach requires either data from multiple stores having different assortments, which may introduce errors due to customer demographics and be contrary to central assortment planning, or using data from different experimental assortments at a single store, potentially costing the store profit opportunities.